Feasibility of Grid-connected Solar-wind Hybrid System with Electric Vehicle Charging Station

Singh, Shakti; Chauhan, Prachi; Singh, Nirbhow Jap

doi:10.35833/mpce.2019.000081

Cited by 78 publications

(28 citation statements)

References 35 publications

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“…The normal operating cell temperature (NOCT) is the cell temperature of PV module when the value of ambient temperature is 20 C and value of solar irradiation is 0.80 kW/m 2 . The following expression can be used for the calculation of temperature in other ambient conditions:

T_{c} ((), t) goodbreak= T_{amb} goodbreak+ \frac{NOCT - 20}{0.8} G_{S},

where,

T_{amb}

is the ambient temperature 26,30 …”

Section: System Descriptionmentioning

confidence: 99%

“…The rate of hydrogen mass flow taken by the FC per hour per unit rating is the major component in the modeling of FCs. The rate of hydrogen mass flow

{\dot{m}}_{H_{2}}^{c}

taken by FC of 1 kW in kg/hr is evaluated as:

{\dot{m}}_{H_{2}}^{c} goodbreak= α_{1} P_{FC}^{r} goodbreak+ α_{2} P_{FC} ((), t),

where

α_{1}

is the intercept coefficient kg/hr/kW rated ,

α_{2}

is cell slope curve kg/hr/kW of the FC, and

P_{FC}^{r}

is taken as rated capacity of FC in kW 26 …”

Section: System Descriptionmentioning

confidence: 99%

“…When the electrolyzer generates hydrogen, then it is saved in the hydrogen tank so that it can be supplied to FCs when there is a need for power generation. The rate of mass flow of hydrogen generated

{\dot{m}}_{H_{2}}^{p}

, in kg/hr by a 1 kW electrolyzer is the major parameter that can be calculated as:

{\dot{m}}_{H_{2}}^{p} goodbreak= \frac{(P_{italicEL}^{r} η_{italicEL} 3600)}{{italicHV}_{H_{2}}},

where

P_{EL}^{r}

is the rated capacity, that is, 1 kW,

η_{EL}

is electrolyzer efficiency, and

{HV}_{H_{2}}

is heating value of hydrogen in MJ/kg 26 …”

Section: System Descriptionmentioning

confidence: 99%

“…

C_{con}

is the purchase value of bi‐directional converter per kW,

N_{pv}, P_{fc}^{r}

, and

P_{el}^{r}

are capacity of solar PV panels, FCs, and electrolyzer, respectively, and

T_{H_{2}}^{r}

is the size of hydrogen storage tank. Further, for the detailed analysis of cost of components, literature can be refereed 26 …”

Section: Operational Strategy and Objective Functionmentioning

confidence: 99%

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System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

Singh

Gupta

2021

Intl J of Energy Research

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Summary In current years, hydrogen‐based power generation has emerged as an alternative energy source for transportation and electricity. Hydrogen‐based power generation along with solar photovoltaic (PV) is a matter of current research. In this manuscript, a hybrid solar, fuel cell, battery with an electrolyzer and hydrogen tank has been developed to cater the electricity demand of a small academic complex. The economic analysis of the project has been performed with detailed mathematical modeling of the system. The hydrogen and energy management is performed on a one‐year complete data. To deduce the sizing of the components, a novel artificial bee colony algorithm with predator effect (ABC‐PO) has been proposed by the authors. The optimal sizing results obtained from the proposed ABC‐PO have been compared with other optimization algorithms, that is, conventional ABC, particle swarm optimization (PSO), and hybrid ABC‐PSO. The results show that electricity demand of 39.4 MWh/yr could be met with a combination of 27 kW solar PV, 24 kW electrolyzer, 10 kW fuel cell, and 57 kWh batteries with a 131 kg of hydrogen tank. The designed system provides a levelized cost of electricity of $0.279/kWh with a minimum loss of power supply probability. A detailed hydrogen and energy management has been discussed to show the efficacy of the system.

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T_{c} ((), t) goodbreak= T_{amb} goodbreak+ \frac{NOCT - 20}{0.8} G_{S},

where,

T_{amb}

is the ambient temperature 26,30 …”

Section: System Descriptionmentioning

confidence: 99%

“…The rate of hydrogen mass flow taken by the FC per hour per unit rating is the major component in the modeling of FCs. The rate of hydrogen mass flow

{\dot{m}}_{H_{2}}^{c}

taken by FC of 1 kW in kg/hr is evaluated as:

{\dot{m}}_{H_{2}}^{c} goodbreak= α_{1} P_{FC}^{r} goodbreak+ α_{2} P_{FC} ((), t),

where

α_{1}

is the intercept coefficient kg/hr/kW rated ,

α_{2}

is cell slope curve kg/hr/kW of the FC, and

P_{FC}^{r}

is taken as rated capacity of FC in kW 26 …”

Section: System Descriptionmentioning

confidence: 99%

{\dot{m}}_{H_{2}}^{p}

, in kg/hr by a 1 kW electrolyzer is the major parameter that can be calculated as:

{\dot{m}}_{H_{2}}^{p} goodbreak= \frac{(P_{italicEL}^{r} η_{italicEL} 3600)}{{italicHV}_{H_{2}}},

where

P_{EL}^{r}

is the rated capacity, that is, 1 kW,

η_{EL}

is electrolyzer efficiency, and

{HV}_{H_{2}}

is heating value of hydrogen in MJ/kg 26 …”

Section: System Descriptionmentioning

confidence: 99%

“…

C_{con}

is the purchase value of bi‐directional converter per kW,

N_{pv}, P_{fc}^{r}

, and

P_{el}^{r}

are capacity of solar PV panels, FCs, and electrolyzer, respectively, and

T_{H_{2}}^{r}

is the size of hydrogen storage tank. Further, for the detailed analysis of cost of components, literature can be refereed 26 …”

Section: Operational Strategy and Objective Functionmentioning

confidence: 99%

See 2 more Smart Citations

System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

Singh

Gupta

2021

Intl J of Energy Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Along with the increase in EVCS installation, more and more EVCS tend to have charging infrastructure, rooftop PV, and energy storage (ES) all together for energy/cost saving and emission reduction (Yasn et al, 2019;Yang and Ribberink, 2019;Sierra et al, 2020). With integrated design and control, a system of this kind is referred to as an EVCS integrated with PV and ES (Colak et al, 2015;Liu et al, 2015;Shariff et al, 2020;Singh et al, 2020;Singh et al, 2021). These EVCSs are mainly distributed in medium-/large-scale communities, business parks, and commercial areas (Brenna et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Tertiary Control for Energy Management of EV Charging Station Integrated With PV and Energy Storage

Dan¹,

Liu

Zhu

et al. 2022

Front. Energy Res.

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Along with the rapid increase in the number of electric vehicles, more and more EV charging stations tend to have charging infrastructure, rooftop photovoltaic and energy storage all together for energy saving and emission reduction. Compared with individual design for each of the components in such kind of systems, an integrated design can result in higher efficiency, increased reliability, and lower total capital cost. This paper mainly focuses on the tertiary control strategy for dynamic state operation, such as PV generation fluctuation and random arrival/leave of EVs. The tertiary control aims to achieve stable operation under dynamic states, as well as to optimize the energy flow in the station to realize maximal operational benefits with constraints such as peak/valley price of electricity, state of discharge limitation of battery, etc. In this paper, four energy management functions in tertiary control level are proposed, and their performance is verified by simulations. By using prediction of PV power and EV load in the following 72 h, a novel tertiary control logic is proposed to optimize PVC and ESC power flow by changing their droop characteristics, so that minimum operational cost for the station can be achieved. Furthermore, a sensitivity analysis is conducted for three parameters, including ES battery capacity, weather influence, and PV and EV load prediction error. The results from sensitivity analysis indicate that ES battery capacity and weather condition lead to a great impact on the operational cost of the integrated charging station, while a typical prediction error of PV power and EV load will not influence the optimization result significantly.

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SPICE‐compliant load flow analysis of power distribution networks with behavioral loads

Manfredi,

Trinchero,

Memon

et al. 2023

Circuit Theory & Apps

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This paper discusses an alternative and Simulation Program with Integrated Circuit Emphasis (SPICE)‐compatible solution for the steady‐state simulation of power distribution networks. It introduces two alternative auxiliary subcircuits that allow to efficiently simulate the network in any simulation tool for circuit analysis. This feature introduces remarkable benefits in terms of simplicity and generality, since it avoids the need for any iterative method, thus bridging the gap between classical industry‐standard and general‐purpose simulators based on circuital equations, such as SPICE, and dedicated tools for the load flow analysis of power systems. The approach is first demonstrated based on a simple illustrative example, for which the performance in terms of accuracy and efficiency is discussed and then applied to the standard IEEE 33‐node power distribution benchmark.

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Feasibility of Grid-connected Solar-wind Hybrid System with Electric Vehicle Charging Station

Cited by 78 publications

References 35 publications

System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

Tertiary Control for Energy Management of EV Charging Station Integrated With PV and Energy Storage

SPICE‐compliant load flow analysis of power distribution networks with behavioral loads

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